Generative AI
Generative AI refers to AI systems capable of creating new content — text, images, audio, video, code, and 3D models — by learning the statistical patterns of their training data. Unlike discriminative models (which classify inputs), generative models learn a distribution over data and can sample new instances from it.
How Generative AI Works
Modern generative AI primarily uses two architectures: Transformers (for text, code, and multimodal content) and Diffusion Models (for images and video). Transformers generate text by predicting the next token given context. Diffusion models learn to reverse a gradual noise-addition process — given a noisy image and a text prompt, they iteratively denoise toward a coherent output. Both are trained on billions of human-created examples.
Key Use Cases
- Text generation (ChatGPT, Claude, Gemini)
- Image creation (DALL-E, Midjourney, Stable Diffusion)
- Code generation (GitHub Copilot, Cursor)
- Video synthesis (Sora, Runway, Kling)
- Music composition (Suno, Udio)
- Drug discovery and molecular design
- Marketing and personalised content at scale
Frequently Asked Questions
- What is generative AI?
- Generative AI is AI that creates new content — text, images, audio, code, or video — rather than just classifying or analysing existing content. ChatGPT writes text; DALL-E generates images; Sora creates video.
- Is generative AI the same as ChatGPT?
- ChatGPT is one application of generative AI. The broader field includes image generators (Midjourney), code assistants (Copilot), video generators (Sora), and music AI (Suno). They all share the ability to create novel content.
- How accurate is generative AI?
- Generative AI excels at fluent, coherent output but can "hallucinate" — producing plausible-sounding but incorrect information. Accuracy has improved significantly with retrieval-augmented generation (RAG) and tool use.
